Automated license plate recognition (ALPR) is a key enabler for several transportation applications. Though being a mature technology, the challenge with ALPR systems is scalability and minimizing human intervention in the existence of challenging noise sources present in license plate images captured under realistic conditions (i.e., field deployed solutions). These include: heavy shadows, non-uniform illumination, challenging optical geometries, partial occlusion, varying contrast, and general imaging noise. These challenging imaging conditions make it difficult to locate the license plate. Given these potential challenges, a number of captured license plate images cannot be recognized by a human, which in turn leads to a waste of review effort and increased cost.
ALPR systems can be employed in a variety of traffic surveillance applications, including toll monitoring, parking management, and detection of different types of traffic violation. FIG. 1 illustrates a high level block diagram providing an overview of operations and components of a prior art ALPR system 10. ALPR systems such as the example prior art system 10 shown in FIG. 1 typically include four stages. In the first stage, as shown at block 12, an image of a vehicle can be captured while the vehicle is moving (e.g., passing a toll booth). In this image acquisition stage, near infrared cameras are typically utilized to capture vehicle images both day and night time under low lighting conditions. In the second stage, as shown at block 14, the captured vehicle image is processed to localize the license plate region in the image. Many different methods may be implemented for license plate localization. After localizing the plate region in the image, the characters are segmented and extracted in the third stage, as shown at block 16. In the final stage, the segmented character images are recognized, as depicted at block 18, utilizing an OCR (Optical Character Recognition) engine trained in an offline phase. Thereafter, as shown at block 20, an operation can be implemented to identify a state associated with the license plate. The OCR engine typically outputs a confidence score for each of the segmented character from which an overall confidence score is calculated for the entire plate. If the overall confidence score is higher than a pre-defined threshold, recognized license plate number are transmitted directly to the rest of the processing pipeline without human interruption as indicated at block 22. When the confidence score is less than the threshold, the license plate image first goes to manual human review process to avoid the serious public relations problem of issuing improper citations.